Modeling the Probability of Dry Lightning‐Induced Wildfires in Tasmania: A Machine Learning Approach

Geophysical Research Letters, Journal Year: 2024, Volume and Issue: 51(16)

Published: Aug. 13, 2024

Abstract Dry lightning is a prevalent episodic natural ignition source for wildfires, particularly in remote regions where such fires can escalate into uncontrollable events, burning extensive areas. In this study, we aimed to understand the interplay of environmental, fuel, and geographical factors evaluating probability fire initiation following dry strikes Tasmania, Australia. We integrated lightning, active records, gridded data on weather, topography binary classification framework both fire‐initiating non‐fire‐causing strikes. Employing statistical machine learning techniques, quantified likelihood due with resampled Random Forest model exhibiting notable performance an ROC‐AUC value 0.98. Our findings highlight how fuel characteristics moisture content associated particular vegetation types influence provide objective approach identifying susceptible ignitions, informing management responses.

Language: Английский

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Forest-fire interactions, impacts, and implications: a focus on mangroves

New Zealand journal of forestry science, Journal Year: 2025, Volume and Issue: 55

Published: March 7, 2025

Background: Fire plays a key role in the world’s wetland ecosystems, affecting fundamental aspects of their ecological functioning. The increased frequency wildfires continues to exert significant influence on succession mangrove ecosystems and spatial distribution species. Numerous studies have attempted highlight effect fires forest ecosystem function integrity; however, results are inconclusive. In particular, it remains uncertain whether direct impacts implications type known for its distinct characteristics low ignition rates due high moisture levels. Methods: We conducted comprehensive review over 120 relevant scholarly articles found through formal searches literature citation databases by surveying publications identify examine interactions, prevalence, globally. also synthesised recurrent numerous goods services offered mangroves highlighted existing gaps directions future research. Results: Mangrove prevalent many countries across world with varying distributions forested areas. While there causes mangroves, most occurrences combination natural dry periods (El Niño events) anthropogenic activities, which may deliberately or accidentally increase fire regimes. There negative effects can affect provided environment society, including habitat loss, pollution, wildlife destruction. However, our findings some cases where positive encouraging nutrient enrichment expansion. Our reports priorities research understanding sustainable management coexistence preservation, conservation, public awareness. Conclusions: Forest phenomena human-induced factors. With predominantly effects, result loss integrity, leading severe economic losses degradation. Emphasis should therefore be placed forestry awareness mitigation dynamic ecosystems.

Language: Английский

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Assessment of land use and land cover (LULC) and vegetation degradation state from wildfire within the scope of forest landscape restoration (FLR) in Cameroon

European Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: 58(1)

Published: May 7, 2025

Language: Английский

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FusionFireNet: A CNN-LSTM Model for Short-Term Wildfire Hotspot Prediction Utilizing Spatio-Temporal Datasets

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 37, P. 101436 - 101436

Published: Dec. 16, 2024

Language: Английский

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Interpretable Artificial Intelligence Models for Predicting Lightning Prone to Inducing Forest Fires

Journal of Atmospheric and Solar-Terrestrial Physics, Journal Year: 2024, Volume and Issue: unknown, P. 106408 - 106408

Published: Dec. 1, 2024

Language: Английский

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Study on the Driving Factors of the Spatiotemporal Pattern in Forest Lightning Fires and 3D Fire Simulation Based on Cellular Automata

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 1857 - 1857

Published: Oct. 23, 2024

Lightning-induced forest fires frequently inflict substantial damage on ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence for such phenomena. To elucidate occurrence patterns of caused by lightning and to prevent fires, this study employs multifaceted approach, including statistical analysis, kernel density estimation, spatial autocorrelation conduct comprehensive examination spatiotemporal distribution lightning-induced Greater Khingan Mountains from 2016–2020. Additionally, geographical detector method is utilized assess explanatory power three main factors: climate, topography, fuel characteristics associated these encompassing both univariate interaction detections. Furthermore, mixed-methods approach adopted, integrating Zhengfei Wang model three-dimensional cellular automaton simulate spread fire events, which further validated through rigorous quantitative verification. The principal findings are follows: (1) Spatiotemporal Distribution Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations incidence fires. monthly concentration incidents most significant May, July, August, demonstrating an upward trajectory. In terms temporal distribution, occurrences predominantly concentrated between 1:00 PM 5:00 PM, conforming normal pattern. Spatially, higher incidences observed western northwestern regions, while eastern southeastern areas exhibit reduced rates. At township level, indicates that Xing’an Town represents prominent hotspot (p = 0.001), whereas Oupu identified cold spot 0.05). (2) Determinants influenced multitude factors. Univariate analysis factors varies significantly, climatic exerting influence, followed topographic characteristics. Interaction factor interactive effects variables notably more than those topographical (3) Three-Dimensional Cellular Automaton Fire Simulation Based Model: This investigation integrates principles into framework dynamic behavior Through validation against empirical demonstrates accuracy rate 83.54% forecasting affected zones.

Language: Английский

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Role of positive outlier cloud-to-ground lightning strokes in initiating forest fires in India

Journal of Earth System Science, Journal Year: 2024, Volume and Issue: 133(4)

Published: Oct. 25, 2024

Language: Английский

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Fuel Load Models for Different Tree Vegetation Types in Sichuan Province Based on Machine Learning

Forests, Journal Year: 2024, Volume and Issue: 16(1), P. 42 - 42

Published: Dec. 29, 2024

(1) Objective: To improve forest fire prevention, this study provides a reference for risk assessment in Sichuan Province. (2) Methods: This research focuses on various vegetation types Given data from 6848 sample plots, five machine learning models—random forest, extreme gradient boosting (XGBoost), k-nearest neighbors, support vector machine, and stacking ensemble (Stacking)—were employed. Bayesian optimization was utilized hyperparameter tuning, resulting models predicting fuel loads (FLs) across different types. (3) Results: The FL model incorporates not only characteristics but also site conditions climate data. Feature importance analysis indicated that structural factors (e.g., canopy closure, diameter at breast height, tree height) dominated cold broadleaf, subtropical mixed forests, while mean annual temperature seasonality) were more influential coniferous forests. Machine learning-based outperform the multiple stepwise regression both fitting ability prediction accuracy. XGBoost performed best coniferous, with coefficient of determination (R2) values 0.79, 0.85, 0.81, 0.83, respectively. Stacking excelled achieving an R2 value 0.82. (4) Conclusions: establishes theoretical foundation capacity It is recommended be applied to predict broadleaf suggested FLs Furthermore, offers management, assessment, prevention control

Language: Английский

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Biased Perception of Macroecological Findings Triggered by the IPCC—The Example of Wildfires

Sustainability, Journal Year: 2024, Volume and Issue: 17(1), P. 134 - 134

Published: Dec. 27, 2024

Global change and disturbance ecology, including the risks benefits of wildfires for humans, sustainability ecosystems biodiversity, is a current research topic in applied science. Fires their impacts are often considered context climate change, carbon dioxide emissions air pollution. Despite significant decline at global scale recent decades (cf. Wildfire Information System (GWIS)), it widespread conviction that burned area increasing due to warming. In an attempt identify how this discrepancy has arisen, we analysed IPCC reports from 2018–2023 via text mining word frequency analyses compared considerations about fire weather with findings ecology public information on internet. Both negativity bias repetition were identified. Numerous examples disasters models indicating increase composed alarming messages. Examples decreasing much less frequently communicated. Important facts ignored, especially summaries policymakers. Measured against fire-ecological conditions nature, trends exaggerated. We therefore call comprising differentiated reflection ecological processes future.

Language: Английский

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Dry Lightning as a Forest Predator: A Review

Research Journal of Engineering and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 65 - 68

Published: Dec. 31, 2024

Lightning discharges have great impact on forest ecology. There are many indications that lightning is main cause for wildfires. Dry or rain free strikes prominent natural source In remote areas dry can escalate into uncontrollable events. often ignites wildfires in vegetation areas. It has been studied by several researchers most destructive and costly The present work the review study of influences variables such as environment, weather, fuel dryness geographical factors their effect also aimed to study. shown be huge wildfires, it well known a growing threat climate continues warm. So, an understanding essential fire management.

Language: Английский

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